Zero-shot learning (ZSL) enables the recognition of unseen categories by leveraging models trained only on labeled seen-class samples in the source domain. Traditional ZSL methods typically assume identical data distributions across source and target domains—an assumption that rarely holds in real-world scenarios and causes dramatic performance degradation under domain shift. While existing generative ZSL methods have achieved promising results, they overlook the fundamental challenge of distribution shift. This paper bridges this gap by proposing GM-CDZSL, a generative framework that unifies semantic-conditioned feature synthesis and multi-source domain alignment for cross-distribution zero-shot learning. Unlike conventional approaches that only align semantic and visual features while ignoring latent domain discrepancies, our method imposes explicit distribution consistency constraints in the embedding space. Specifically, we design a set of one-vs-all domain discriminators and construct a distribution-aware loss based on empirical H-divergence to mitigate domain gaps and learn domain-invariant representations. Extensive experiments on multiple public benchmarks demonstrate that our method achieves consistent performance improvements over representative ZSL and domain generalization baselines, offering a practical solution to realistic cross-distribution zero-shot recognition tasks.
Wu et al. (2026) studied this question.